Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis

• Published in: Journal of environmental chemical engineering 2022-06, Vol.10 (3), p.108025, Article 108025
• Main Authors: Balsora, Hemant Kumar, S, Kartik, Dua, Vivek, Joshi, Jyeshtharaj Bhalchandra, Kataria, Gaurav, Sharma, Abhishek, Chakinala, Anand Gupta
• Format: Article
• Language: English
• Subjects: Artificial neural network; Biomass; Pyrolysis kinetics; Thermogravimetric analysis
• ISSN: 2213-3437; 2213-3437
• DOI: 10.1016/j.jece.2022.108025

The pyrolytic behavior of lignocellulosic biomass is highly complex, and its kinetic behavior varies with operating conditions and the type of biomass. To reduce timescales, cost and rigorous calculations associated with new set of experimentation used for the estimation of kinetic parameters, model-based predictions are recommended. In the present work, Artificial Neural Network (ANN) based machine learning models are developed to predict the biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomass were used to develop and test the networks. Four models were developed in this study based on proximate analysis (ANN-1), ultimate analysis (ANN-2), combined proximate and ultimate analysis (ANN-3) and the combined proximate, ultimate, and biochemical analysis (ANN-4). A total of 704 kinetic datasets were extracted and recalculated with the Coats-Redfern Method from which 662, 585, 465 and 133 datasets were used to develop models sequentially. The developed models, in particular ANN-3 and ANN-4 have shown a competitive prediction capability (R2 ~ 0.99, RRMSE